1. Carga la librería fpp2 y utiliza la información de los primeros 20 días de la base de datos correspondiente a la demanda diaria de electricidad de Victoria, en Australia, para 2014 (datos20 <- head(elecdaily,20))

Carga de librerías:

library(fpp2)
library(fpp3)
library(forecast)
library(readxl)
library(TSstudio)
library(ggseas)
library(data.table)
library(urca)
library(formattable)
library(janitor)

Carga de base de datos:

datos20 <- 
  head(elecdaily,20)
  1. Grafica la serie de tiempo.
autoplot(datos20[,"Demand"]) + 
  ggtitle("Total electricity demand in GW for Victoria, Australia, every day during 2014") +
  xlab("Time") + 
  ylab("Total electricity demand [GW]")

  1. Has un modelo de regresión lineal con las variables disponibles en la base de datos, a fin de pronosticar la demanda de electricidad.
# Transformación de datos a tsibble

datos20_tbl <- 
  as_tsibble(datos20,
             pivot_longer = FALSE)

# Regresión lineal múltiple en datos ts

fit.datos20 <- tslm(Demand ~ 
         Temperature + WorkDay,
       data = datos20)

summary(fit.datos20)
## 
## Call:
## tslm(formula = Demand ~ Temperature + WorkDay, data = datos20)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.670 -14.041   5.072  15.947  29.648 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  41.1689    17.3294   2.376   0.0295 *  
## Temperature   6.2842     0.6592   9.533  3.1e-08 ***
## WorkDay      17.5890    11.1214   1.582   0.1322    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21.14 on 17 degrees of freedom
## Multiple R-squared:  0.888,  Adjusted R-squared:  0.8749 
## F-statistic: 67.42 on 2 and 17 DF,  p-value: 8.263e-09
# Regresión lineal múltiple en tsibble

fit.datos20_tbl <- datos20_tbl %>% 
  model(
    TSLM(Demand ~ 
         Temperature + WorkDay)
    )

report(fit.datos20_tbl)
## Series: Demand 
## Model: TSLM 
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.670 -14.041   5.072  15.947  29.648 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  41.1689    17.3294   2.376   0.0295 *  
## Temperature   6.2842     0.6592   9.533  3.1e-08 ***
## WorkDay      17.5890    11.1214   1.582   0.1322    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21.14 on 17 degrees of freedom
## Multiple R-squared: 0.888,   Adjusted R-squared: 0.8749
## F-statistic: 67.42 on 2 and 17 DF, p-value: 8.2627e-09
#Gráfica de observaciones reales y estimados con Regresión Lineal Múltiple

autoplot(datos20[,"Demand"], series = "Data") +
  autolayer(fitted(fit.datos20), series = "Fitted") + 
  xlab("Day") +
  ylab("") + 
  ggtitle("Total electricity demand in GW for Victoria, Australia, every day during 2014") + 
  guides(colour = guide_legend(title = ""))

  1. Interpreta los coeficientes de tu regresión. R: En Victoria, Australia; un grado Celsius adicional en la temperatura máxima se traduce en un incremento de 6.28 GW en la demanda diaria de electricidad. La variable WorkDay, que señala si se hace referencia a días hábiles o inhábiles, no es estadísticamente significativa.

  2. Analiza los residuales: grafícalos e interprétalos.

# Gráfica de los residuales
fit.datos20_tbl %>% gg_tsresiduals()

# La primera gráfica nos sugiere que los residuales podrían no estarse comportando de forma aleatoria, pues hay una tendencia alcista en los mismos.

# Prueba Ljung-Box
## Hipótesis nula: serie estacionaria
## Hipótesis alternativa: serie NO estacionaria

augment(fit.datos20_tbl) %>% 
  features(.innov, ljung_box, 
           lag = ifelse(2*7 > nrow(datos20_tbl)/5,
                        nrow(datos20_tbl)/5,
                        2*7), # De acuerdo con el libro de texto FPP3, se utiliza el número de observaciones dividido entre 5 si este resultado es menor que 10 o que 2m (cuando la serie tiene estacionalidad y m es el periodo de estacionalidad.)
           dof = 2) # Número de parámetros del modelo.
# Gráfico entre las variables independientes y los residuales
datos20_tbl %>% 
  left_join(residuals(fit.datos20_tbl),
            by = "index") %>% 
  pivot_longer(WorkDay:Temperature,
               names_to = "regressor",
               values_to = "x") %>% 
  ggplot(aes(x = x, y = .resid)) +
  geom_point() + 
  facet_wrap(. ~ regressor, scales = "free_x") +
  labs(y = "Residuals", x = "")

# Gráfico entre valores estimados y residuales
augment(fit.datos20_tbl) %>% 
  ggplot(aes(x = .fitted, y = .resid)) +
  geom_point() +
  labs(x = "Fitted", y = "Residuals")

R: A partir de la gráfica de residuales, se sugiere que quizás los mismos tienen una tendencia. El correlograma no sugiere que haya correlaciones estadísticamente significativas entre los diferentes lags utilizados, y el histograma sugiere que pudiera haber una distribución que se aproxime a la distribución normal de los mismos residuales. Al llevar a cabo la prueba Ljung-Box, considerando que el pvalue es mayor a 0.05, concluimos que la hipótesis nula se acepta, por lo tanto la serie es estacionaria. No se aprecia que exista correlación alguna entre las variables independientes y los residuales, ni tampoco entre las estimaciones y los residuales.

  1. Utiliza tu moldeo para pronosticar la demanda de electricidad, para el siguiente día, si la temperatura fuera de 15° grados y compáralo con el pronóstico de 35°. ¿Cuáles son los pronósticos? ¿Parecen realistas?
# Genero escenarios para pronosticar la demanda de energía eléctricica considerando que el día siguiente es un día hábil con 15° o con 35° de temperatura.

future_scenarios <- scenarios(
  LowTemp = new_data(datos20_tbl, 1) %>%
  mutate(WorkDay = 1, 
         Temperature = 15),
  HiTemp = new_data(datos20_tbl, 1) %>%
  mutate(WorkDay = 1, 
         Temperature = 35),
  names_to = "Scenario"
  )

# Hago los pronósticos correspondientes a cada escenario
fc <- forecast(fit.datos20_tbl,
               new_data = future_scenarios)

# Genero las gráficas de los pronósticos.
datos20_tbl %>% 
  autoplot(Demand) +
  autolayer(fc) +
  labs(title = "Total electricity demand in GW for Victoria, Australia",
       y = "Electricity demand [GW]")

R: El pronóstico de la demanda de energía eléctrica para el día siguiente asumiendo que la temperatura máxima es de 15°, si bien es consistente con la tendencia del comportamiento de los datos, no se podría afirmar que es un comportamiento realista. Esto pues no se cuenta con ningún registro dentro del dataframe datos20 que corresponda a dicha temperatura máxima. Por lo tanto, para hacer ese pronóstico, se tendría que suponer que el comportamiento de los datos de demanda de electricidad vs temperatura máxima seguiría manteniendo una tendencia lineal en esos niveles de temperatura. Para el pronóstico de 35°, por otro lado, si se cuenta con registros que rodean esta temperatura máxima; y dichos registros efectivamente rodean también el pronóstico de demanda de electricidad. Por lo tanto, este pronóstico parece una aproximación realista considerando que esa fuera la temperatura máxima para ese día.

  1. Obtén las mediciones de error del modelo y comenta si los resultados son útiles. ¿Son mejores que si usamos el método naive?
# Calculo porcentaje de error de pronóstico considerando que al siguiente día la temperatura sería 15°C
error.15 <- 
  formattable::percent(
    (fc[1,5] - elecdaily[21,1])/
      elecdaily[21,1]
    )

view(error.15)

# Calculo porcentaje de error de pronóstico considerando que al siguiente día la temperatura sería 35°C
error.35 <-
  formattable::percent(
    (fc[2,5] - elecdaily[21,1])/
      elecdaily[21,1]
  )

view(error.35)

# Realizo el modelo NAIVE
naive_model <-
  datos20_tbl %>% 
  model(
    naive = NAIVE(Demand)
  )

# Calculo el pronóstico del modelo NAIVE
naive_forecast <-
  naive_model %>% 
  forecast(h=1)

# Genero las gráficas del pronóstico con el modelo NAIVE.
datos20_tbl %>% 
  autoplot(Demand) +
  autolayer(naive_forecast) +
  labs(title = "Total electricity demand in GW for Victoria, Australia",
       y = "Electricity demand [GW]")

# Calculo porcentaje de error de pronóstico considerando el modelo NAIVE
error.naive <- 
  formattable::percent(
    (naive_forecast[1,4] - elecdaily[21,1])/
      elecdaily[21,1]
    )

View(error.naive)

R: Los resultados de la regresión no resultan ser tan útiles como el modelo NAIVE en este caso, pues las estimaciones de temperatura que se utilizaron para el siguiente día con la regresión fueron de 15°C y de 35°C cuando la temperatura máxima real fue en realidad de 23.1°C. Con esto, el método NAIVE de utilizar la última observación como pronóstico resultó más acertado que la regresión lineal, con un porcentaje de error del 2.75% a comparación de los errores de 31.19% y 25.34% para los pronósticos de la regresion lineal a 15°C y a 35°C respectivamente.

  1. Carga la base de datos “Elecmensual 20190618”; separa los componentes de la serie de tiempo e incluye la gráfica (es la demanda mensual ficticia de electricidad de Victoria, en Australia, desde enero de 1950).
# Carga de la base de datos en ts

Elecmensual_20190618.xls <- 
  read_excel("Elecmensual 20190618.xlsx") %>% 
  select(Demand)

Elecmensual_20190618.ts <- 
  ts(
    Elecmensual_20190618.xls,
    start = c(1950,1),
    deltat = 1/12
    )

# Grafico la serie de tiempo original

ts_plot(Elecmensual_20190618.ts[,"Demand"],
        slider = TRUE,
        type = "multiple",
        title = "Demanda mensual de electricidad de Victoria, en Australia, desde enero de 1950",
        Ytitle = "Demanda de electricidad [GW]",
        Xtitle = "Mes"
        )
# Descompongo la serie de tiempo en forma aditiva y multiplicativa

ts_decompose(Elecmensual_20190618.ts[,"Demand"], type = "both")
# Ajusto formato de serie de tiempo a data.table

Elecmensual_20190618.dt <- as.data.table(Elecmensual_20190618.ts)

# Ajusto los números de meses a fechas según la descripción provista
Elecmensual_20190618.dt <- Elecmensual_20190618.dt %>% 
  mutate(Month = 
           yearmonth(
             seq(
               as.Date("1950-01-01"),
               by = "month",
               length.out = 730
               )
             )
         )

# Tendencia
Elecmensual_20190618.dt[,trend := zoo::rollmean(Demand, 12, fill=NA, align = "right")]
## Warning in `[.data.table`(Elecmensual_20190618.dt, , `:=`(trend,
## zoo::rollmean(Demand, : Invalid .internal.selfref detected and fixed by taking
## a (shallow) copy of the data.table so that := can add this new column by
## reference. At an earlier point, this data.table has been copied by R (or was
## created manually using structure() or similar). Avoid names<- and attr<- which
## in R currently (and oddly) may copy the whole data.table. Use set* syntax
## instead to avoid copying: ?set, ?setnames and ?setattr. If this message doesn't
## help, please report your use case to the data.table issue tracker so the root
## cause can be fixed or this message improved.
knitr::kable(tail(Elecmensual_20190618.dt))
Demand Month trend
178.8567 2010 may. 217.4504
185.7280 2010 jun. 212.9069
200.5128 2010 jul. 209.3583
203.2730 2010 ago. 207.4522
198.2401 2010 sep. 204.9424
198.3702 2010 oct. 203.3041
# Datos sin la tendencia
Elecmensual_20190618.dt[,`:=`( detrended_a = Demand - trend,  detrended_m = Demand / trend )]

knitr::kable(tail(Elecmensual_20190618.dt))
Demand Month trend detrended_a detrended_m
178.8567 2010 may. 217.4504 -38.593708 0.8225172
185.7280 2010 jun. 212.9069 -27.178942 0.8723436
200.5128 2010 jul. 209.3583 -8.845483 0.9577495
203.2730 2010 ago. 207.4522 -4.179183 0.9798547
198.2401 2010 sep. 204.9424 -6.702250 0.9672969
198.3702 2010 oct. 203.3041 -4.933950 0.9757312
# Estacionalidad
Elecmensual_20190618.dt[,`:=`(seasonal_a = mean(detrended_a, na.rm = TRUE),
                seasonal_m = mean(detrended_m, na.rm = TRUE)), 
          by=.(month(Month)) ]

knitr::kable(tail(Elecmensual_20190618.dt))
Demand Month trend detrended_a detrended_m seasonal_a seasonal_m
178.8567 2010 may. 217.4504 -38.593708 0.8225172 -2.4727042 0.9893817
185.7280 2010 jun. 212.9069 -27.178942 0.8723436 -1.3238808 0.9951545
200.5128 2010 jul. 209.3583 -8.845483 0.9577495 0.8239771 1.0044215
203.2730 2010 ago. 207.4522 -4.179183 0.9798547 1.1708394 1.0051108
198.2401 2010 sep. 204.9424 -6.702250 0.9672969 1.8524413 1.0076365
198.3702 2010 oct. 203.3041 -4.933950 0.9757312 -0.6260301 0.9977708
# Parte aleatoria
Elecmensual_20190618.dt[,`:=`( residual_a = detrended_a - seasonal_a, 
                 residual_m = detrended_m / seasonal_m )]

knitr::kable(tail(Elecmensual_20190618.dt))
Demand Month trend detrended_a detrended_m seasonal_a seasonal_m residual_a residual_m
178.8567 2010 may. 217.4504 -38.593708 0.8225172 -2.4727042 0.9893817 -36.121004 0.8313447
185.7280 2010 jun. 212.9069 -27.178942 0.8723436 -1.3238808 0.9951545 -25.855061 0.8765911
200.5128 2010 jul. 209.3583 -8.845483 0.9577495 0.8239771 1.0044215 -9.669460 0.9535335
203.2730 2010 ago. 207.4522 -4.179183 0.9798547 1.1708394 1.0051108 -5.350023 0.9748723
198.2401 2010 sep. 204.9424 -6.702250 0.9672969 1.8524413 1.0076365 -8.554691 0.9599661
198.3702 2010 oct. 203.3041 -4.933950 0.9757312 -0.6260301 0.9977708 -4.307920 0.9779111
# Gráficas
ggsdc(Elecmensual_20190618.dt, 
      aes(x = Month, y = Demand), 
      method = "decompose", 
      frequency = 12, 
      s.window = 8, 
      type = "additive") + 
  geom_line() +
  ggtitle("Aditiva") + 
  theme_minimal()
## Warning in if (class(data[, xvar]) == "Date" & (is.null(frequency))) {: la
## condición tiene longitud > 1 y sólo el primer elemento será usado
## Warning: Removed 6 row(s) containing missing values (geom_path).

ggsdc(Elecmensual_20190618.dt, 
      aes(x = Month, y = Demand), 
      method = "decompose", 
      frequency = 12, 
      s.window = 8, 
      type = "multiplicative") + 
  geom_line() +
  ggtitle("Multiplicativa") + 
  theme_minimal()
## Warning in if (class(data[, xvar]) == "Date" & (is.null(frequency))) {: la
## condición tiene longitud > 1 y sólo el primer elemento será usado

## Warning in if (class(data[, xvar]) == "Date" & (is.null(frequency))) {: Removed
## 6 row(s) containing missing values (geom_path).

#¿Es aditiva o multiplicativa?

ggAcf(Elecmensual_20190618.dt$residual_a)

ggAcf(Elecmensual_20190618.dt$residual_m)

Elecmensual_20190618.dt$residual_a %>% ur.kpss() %>% summary()
## 
## ####################### 
## # KPSS Unit Root Test # 
## ####################### 
## 
## Test is of type: mu with 6 lags. 
## 
## Value of test-statistic is: 0.0457 
## 
## Critical value for a significance level of: 
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739
Elecmensual_20190618.dt$residual_m %>% ur.kpss() %>% summary()
## 
## ####################### 
## # KPSS Unit Root Test # 
## ####################### 
## 
## Test is of type: mu with 6 lags. 
## 
## Value of test-statistic is: 0.0587 
## 
## Critical value for a significance level of: 
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739
# Carga de la base de datos en ts

Elecmensual_20190618.xls <- 
  read_excel("Elecmensual 20190618.xlsx") %>% 
  select(Demand)

Elecmensual_20190618.ts <- 
  ts(
    Elecmensual_20190618.xls,
    start = c(1950,1),
    deltat = 1/12
    )

Elecmensual_20190618.ts.trans <- 
  log(Elecmensual_20190618.ts,
      base = 10)

# Ajusto formato de serie de tiempo a data.table

Elecmensual_20190618.dt.trans <- as.data.table(Elecmensual_20190618.ts.trans)

# Ajusto los números de meses a fechas según la descripción provista
Elecmensual_20190618.dt.trans <- Elecmensual_20190618.dt.trans %>% 
  mutate(Month = 
           yearmonth(
             seq(
               as.Date("1950-01-01"),
               by = "month",
               length.out = 730
               )
             )
         )

# Tendencia
Elecmensual_20190618.dt.trans[,trend := zoo::rollmean(Demand, 12, fill=NA, align = "right")]
## Warning in `[.data.table`(Elecmensual_20190618.dt.trans, , `:=`(trend,
## zoo::rollmean(Demand, : Invalid .internal.selfref detected and fixed by taking
## a (shallow) copy of the data.table so that := can add this new column by
## reference. At an earlier point, this data.table has been copied by R (or was
## created manually using structure() or similar). Avoid names<- and attr<- which
## in R currently (and oddly) may copy the whole data.table. Use set* syntax
## instead to avoid copying: ?set, ?setnames and ?setattr. If this message doesn't
## help, please report your use case to the data.table issue tracker so the root
## cause can be fixed or this message improved.
knitr::kable(tail(Elecmensual_20190618.dt.trans))
Demand Month trend
2.252505 2010 may. 2.335101
2.268877 2010 jun. 2.325785
2.302142 2010 jul. 2.318815
2.308080 2010 ago. 2.314956
2.297191 2010 sep. 2.309837
2.297476 2010 oct. 2.306418
# Datos sin la tendencia
Elecmensual_20190618.dt.trans[,`:=`( detrended_a = Demand - trend )]

knitr::kable(tail(Elecmensual_20190618.dt.trans))
Demand Month trend detrended_a
2.252505 2010 may. 2.335101 -0.0825954
2.268877 2010 jun. 2.325785 -0.0569078
2.302142 2010 jul. 2.318815 -0.0166733
2.308080 2010 ago. 2.314956 -0.0068766
2.297191 2010 sep. 2.309837 -0.0126460
2.297476 2010 oct. 2.306418 -0.0089413
# Estacionalidad
Elecmensual_20190618.dt.trans[,`:=`(seasonal_a = mean(detrended_a, na.rm = TRUE)
                              ), 
          by=.(month(Month)) ]

knitr::kable(tail(Elecmensual_20190618.dt.trans))
Demand Month trend detrended_a seasonal_a
2.252505 2010 may. 2.335101 -0.0825954 -0.0047661
2.268877 2010 jun. 2.325785 -0.0569078 -0.0023854
2.302142 2010 jul. 2.318815 -0.0166733 0.0010789
2.308080 2010 ago. 2.314956 -0.0068766 0.0021100
2.297191 2010 sep. 2.309837 -0.0126460 0.0031991
2.297476 2010 oct. 2.306418 -0.0089413 -0.0010349
# Parte aleatoria
Elecmensual_20190618.dt.trans[,`:=`( residual_a = detrended_a - seasonal_a)]

knitr::kable(tail(Elecmensual_20190618.dt.trans))
Demand Month trend detrended_a seasonal_a residual_a
2.252505 2010 may. 2.335101 -0.0825954 -0.0047661 -0.0778292
2.268877 2010 jun. 2.325785 -0.0569078 -0.0023854 -0.0545223
2.302142 2010 jul. 2.318815 -0.0166733 0.0010789 -0.0177522
2.308080 2010 ago. 2.314956 -0.0068766 0.0021100 -0.0089866
2.297191 2010 sep. 2.309837 -0.0126460 0.0031991 -0.0158451
2.297476 2010 oct. 2.306418 -0.0089413 -0.0010349 -0.0079065
# Gráficas
ggsdc(Elecmensual_20190618.dt.trans, 
      aes(x = Month, y = Demand), 
      method = "decompose", 
      frequency = 12, 
      s.window = 8, 
      type = "additive") + 
  geom_line() +
  ggtitle("Aditiva") + 
  theme_minimal()
## Warning in if (class(data[, xvar]) == "Date" & (is.null(frequency))) {: la
## condición tiene longitud > 1 y sólo el primer elemento será usado
## Warning: Removed 6 row(s) containing missing values (geom_path).

#¿Es multiplicativa?
ggAcf(Elecmensual_20190618.dt.trans$residual_a)

Elecmensual_20190618.dt.trans$residual_a %>% ur.kpss() %>% summary()
## 
## ####################### 
## # KPSS Unit Root Test # 
## ####################### 
## 
## Test is of type: mu with 6 lags. 
## 
## Value of test-statistic is: 0.0547 
## 
## Critical value for a significance level of: 
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739
  1. Obtén los datos desestacionalizados y muestra en una tabla los datos originales y los desestacionalizados. Calcula la diferencia entre ellos y gráfica ambas series (original y desestacionalizada) en una gráfica.
comparativa_df <- 
  data.frame(fecha = yearmonth(
             seq(
               as.Date("1950-01-01"),
               by = "month",
               length.out = 730
               )
             ),
             Elecmensual_20190618.ts)

comparativa_df$desestacionalizados <- 
  seasadj(decompose(Elecmensual_20190618.ts, "multiplicative"))

comparativa_df_restas <- dplyr::summarise(comparativa_df,
                         restas = Demand - desestacionalizados)

comparativa_df <- clean_names(comparativa_df)
comparativa_df_restas <- clean_names(comparativa_df_restas)

comparativa_df <- cbind(comparativa_df, comparativa_df_restas)

knitr::kable(comparativa_df)
fecha demand desestacionalizados restas
1950 ene. 174.8963 174.9988 -0.10251801
1950 feb. 188.5909 187.4366 1.15430131
1950 mar. 188.9169 187.5912 1.32570098
1950 abr. 173.8142 173.1173 0.69694883
1950 may. 169.5152 170.9687 -1.45354768
1950 jun. 195.7288 196.6410 -0.91219118
1950 jul. 199.9029 199.2454 0.65747048
1950 ago. 205.3375 204.3724 0.96508480
1950 sep. 228.0782 225.9980 2.08020819
1950 oct. 258.5984 258.4784 0.12002321
1950 nov. 201.7970 203.7739 -1.97689690
1950 dic. 187.6298 189.8154 -2.18555976
1951 ene. 254.6636 254.8129 -0.14927477
1951 feb. 322.2323 320.2600 1.97227525
1951 mar. 343.9934 341.5795 2.41393114
1951 abr. 347.6376 346.2437 1.39393454
1951 may. 332.9455 335.8004 -2.85491896
1951 jun. 219.7517 220.7758 -1.02414955
1951 jul. 186.9816 186.3666 0.61497298
1951 ago. 228.4876 227.4137 1.07389011
1951 sep. 222.3650 220.3369 2.02810043
1951 oct. 220.4669 220.3646 0.10232525
1951 nov. 240.5806 242.9374 -2.35683901
1951 dic. 224.6652 227.2822 -2.61695755
1952 ene. 181.1498 181.2560 -0.10618359
1952 feb. 182.2231 181.1078 1.11532615
1952 mar. 230.2226 228.6070 1.61555862
1952 abr. 309.3744 308.1339 1.24050926
1952 may. 243.2239 245.3095 -2.08558014
1952 jun. 258.3896 259.5938 -1.20422091
1952 jul. 259.3053 258.4525 0.85284196
1952 ago. 241.3185 240.1843 1.13419525
1952 sep. 264.5050 262.0926 2.41244217
1952 oct. 264.9556 264.8326 0.12297378
1952 nov. 221.6807 223.8524 -2.17168683
1952 dic. 237.1498 239.9122 -2.76238135
1953 ene. 276.4134 276.5754 -0.16202373
1953 feb. 283.3155 281.5814 1.73407864
1953 mar. 272.5084 270.5961 1.91229399
1953 abr. 243.6298 242.6529 0.97689086
1953 may. 223.0845 224.9974 -1.91289015
1953 jun. 238.7871 239.9000 -1.11286375
1953 jul. 252.2366 251.4070 0.82959336
1953 ago. 250.9030 249.7238 1.17924233
1953 sep. 242.4558 240.2445 2.21134042
1953 oct. 214.2726 214.1731 0.09945029
1953 nov. 175.2935 177.0108 -1.71725633
1953 dic. 216.4104 218.9312 -2.52080353
1954 ene. 232.6376 232.7740 -0.13636391
1954 feb. 222.3622 221.0012 1.36100405
1954 mar. 210.2031 208.7280 1.47507425
1954 abr. 211.6370 210.7884 0.84860822
1954 may. 186.7995 188.4013 -1.60175595
1954 jun. 178.2322 179.0628 -0.83064853
1954 jul. 221.6854 220.9563 0.72911202
1954 ago. 237.5595 236.4430 1.11652798
1954 sep. 220.5093 218.4981 2.01117535
1954 oct. 216.7880 216.6874 0.10061776
1954 nov. 214.8875 216.9926 -2.10513750
1954 dic. 193.5388 195.7932 -2.25438930
1955 ene. 183.3550 183.4625 -0.10747620
1955 feb. 228.3825 226.9846 1.39785227
1955 mar. 255.3741 253.5820 1.79205616
1955 abr. 233.9622 233.0241 0.93812635
1955 may. 216.4703 218.3265 -1.85617515
1955 jun. 215.9607 216.9672 -1.00648165
1955 jul. 194.5699 193.9300 0.63993052
1955 ago. 200.7804 199.8367 0.94366647
1955 sep. 213.9160 211.9650 1.95104055
1955 oct. 240.5818 240.4701 0.11166117
1955 nov. 220.3766 222.5355 -2.15891126
1955 dic. 219.1226 221.6750 -2.55239593
1956 ene. 223.9310 224.0623 -0.13126041
1956 feb. 191.9780 190.8030 1.17503260
1956 mar. 172.9602 171.7465 1.21372681
1956 abr. 212.6693 211.8166 0.85274747
1956 may. 216.7992 218.6582 -1.85899538
1956 jun. 215.9562 216.9627 -1.00646068
1956 jul. 226.8287 226.0827 0.74602807
1956 ago. 221.9756 220.9323 1.04328376
1956 sep. 186.2491 184.5504 1.69870204
1956 oct. 179.2746 179.1914 0.08320668
1956 nov. 213.5667 215.6589 -2.09219833
1956 dic. 214.6133 217.1132 -2.49987045
1957 ene. 217.4129 217.5403 -0.12743973
1957 feb. 221.7905 220.4330 1.35750486
1957 mar. 214.8814 213.3735 1.50790365
1957 abr. 185.2121 184.4694 0.74265138
1957 may. 181.7951 183.3539 -1.55884455
1957 jun. 230.6637 231.7387 -1.07500476
1957 jul. 253.2901 252.4570 0.83305827
1957 ago. 235.5289 234.4219 1.10698417
1957 sep. 222.7004 220.6692 2.03115947
1957 oct. 218.9320 218.8304 0.10161286
1957 nov. 192.1518 194.0342 -1.88240805
1957 dic. 182.5951 184.7220 -2.12691429
1958 ene. 218.4745 218.6026 -0.12806201
1958 feb. 226.2018 224.8173 1.38450494
1958 mar. 225.5993 224.0162 1.58311518
1958 abr. 223.8808 222.9831 0.89770261
1958 may. 220.3987 222.2886 -1.88986013
1958 jun. 189.7302 190.6144 -0.88423479
1958 jul. 180.7974 180.2028 0.59463346
1958 ago. 212.9014 211.9008 1.00063508
1958 sep. 216.1034 214.1324 1.97099093
1958 oct. 218.3603 218.2590 0.10134751
1958 nov. 215.1137 217.2211 -2.10735346
1958 dic. 177.7474 179.8178 -2.07044705
1959 ene. 183.7940 183.9017 -0.10773353
1959 feb. 178.9498 177.8545 1.09529138
1959 mar. 181.3320 180.0595 1.27247488
1959 abr. 219.8456 218.9641 0.88152253
1959 may. 218.9037 220.7807 -1.87704091
1959 jun. 217.9708 218.9866 -1.01584969
1959 jul. 189.6816 189.0577 0.62385315
1959 ago. 189.3522 188.4622 0.88995401
1959 sep. 186.1761 184.4781 1.69803624
1959 oct. 222.7253 222.6219 0.10337344
1959 nov. 226.8940 229.1168 -2.22275873
1959 dic. 227.8383 230.4922 -2.65391862
1960 ene. 234.1514 234.2887 -0.13725125
1960 feb. 234.8755 233.4379 1.43759374
1960 mar. 205.5938 204.1511 1.44272906
1960 abr. 203.6670 202.8503 0.81665063
1960 may. 237.8857 239.9255 -2.03980650
1960 jun. 240.6153 241.7367 -1.12138405
1960 jul. 240.3634 239.5729 0.79054301
1960 ago. 241.3502 240.2159 1.13434424
1960 sep. 236.7497 234.5904 2.15929741
1960 oct. 206.3455 206.2497 0.09577109
1960 nov. 192.7160 194.6039 -1.88793521
1960 dic. 229.3544 232.0260 -2.67157854
1961 ene. 230.0164 230.1512 -0.13482746
1961 feb. 224.4637 223.0898 1.37386662
1961 mar. 216.9592 215.4367 1.52248435
1961 abr. 212.6305 211.7779 0.85259189
1961 may. 188.9991 190.6197 -1.62061693
1961 jun. 181.5075 182.3534 -0.84591302
1961 jul. 217.0623 216.3484 0.71390688
1961 ago. 224.3771 223.3225 1.05457079
1961 sep. 221.9410 219.9168 2.02423330
1961 oct. 222.4899 222.3866 0.10326418
1961 nov. 221.5659 223.7365 -2.17056220
1961 dic. 195.6669 197.9461 -2.27917795
1962 ene. 190.3111 190.4227 -0.11155362
1962 feb. 226.0510 224.6674 1.38358195
1962 mar. 226.9175 225.3251 1.59236549
1962 abr. 230.2927 229.3693 0.92341263
1962 may. 234.6604 236.6726 -2.01215041
1962 jun. 231.8486 232.9291 -1.08052697
1962 jul. 201.0383 200.3771 0.66120476
1962 ago. 199.7129 198.7743 0.93864922
1962 sep. 232.5876 230.4663 2.12133659
1962 oct. 235.0114 234.9023 0.10907579
1962 nov. 230.9970 233.2600 -2.26295362
1962 dic. 232.0875 234.7909 -2.70341439
1963 ene. 234.6715 234.8091 -0.13755611
1963 feb. 202.4323 201.1933 1.23901985
1963 mar. 196.7197 195.3392 1.38045616
1963 abr. 206.5046 205.6766 0.82802866
1963 may. 235.7782 237.7999 -2.02173525
1963 jun. 241.0994 242.2230 -1.12364019
1963 jul. 243.6167 242.8155 0.80124295
1963 ago. 237.2126 236.0977 1.11489755
1963 sep. 212.1366 210.2018 1.93481137
1963 oct. 203.3582 203.2638 0.09438459
1963 nov. 241.3206 243.6847 -2.36408839
1963 dic. 244.7568 247.6078 -2.85098963
1964 ene. 244.5883 244.7317 -0.14336899
1964 feb. 252.8974 251.3495 1.54789971
1964 mar. 239.6948 238.0128 1.68202861
1964 abr. 211.0494 210.2031 0.84625210
1964 may. 205.6888 207.4525 -1.76372666
1964 jun. 245.3576 246.5011 -1.14348546
1964 jul. 261.7745 260.9135 0.86096303
1964 ago. 248.2476 247.0808 1.16676197
1964 sep. 243.3896 241.1697 2.21985723
1964 oct. 241.5915 241.4794 0.11212981
1964 nov. 218.8741 221.0183 -2.14419208
1964 dic. 220.2953 222.8614 -2.56605584
1965 ene. 255.0056 255.1551 -0.14947524
1965 feb. 254.8101 253.2505 1.55960670
1965 mar. 245.0865 243.3666 1.71986420
1965 abr. 247.8669 246.8730 0.99388051
1965 may. 249.0520 251.1876 -2.13555455
1965 jun. 218.4067 219.4246 -1.01788119
1965 jul. 212.9148 212.2145 0.70026596
1965 ago. 241.2600 240.1261 1.13392030
1965 sep. 242.9725 240.7564 2.21605303
1965 oct. 257.5991 257.4795 0.11955941
1965 nov. 257.9806 260.5079 -2.52729747
1965 dic. 247.6547 250.5394 -2.88474510
1966 ene. 227.0290 227.1621 -0.13307635
1966 feb. 220.9397 219.5874 1.35229740
1966 mar. 258.8287 257.0124 1.81629839
1966 abr. 264.1827 263.1234 1.05930253
1966 may. 253.5232 255.6971 -2.17389390
1966 jun. 259.8050 261.0158 -1.21081736
1966 jul. 256.2355 255.3928 0.84274554
1966 ago. 223.4825 222.4321 1.05036618
1966 sep. 215.7748 213.8068 1.96799390
1966 oct. 258.8278 258.7077 0.12012969
1966 nov. 268.9846 271.6197 -2.63509775
1966 dic. 267.2355 270.3483 -3.11282726
1967 ene. 269.3544 269.5123 -0.15788600
1967 feb. 253.2748 251.7246 1.55020965
1967 mar. 223.1568 221.5908 1.56597524
1967 abr. 211.2160 210.3691 0.84692012
1967 may. 242.0725 244.1482 -2.07570719
1967 jun. 246.4046 247.5530 -1.14836499
1967 jul. 237.8841 237.1017 0.78238872
1967 ago. 239.6187 238.4925 1.12620620
1967 sep. 263.3148 260.9132 2.40158684
1967 oct. 227.5769 227.4713 0.10562521
1967 nov. 221.6350 223.8062 -2.17123913
1967 dic. 256.6197 259.6089 -2.98917171
1968 ene. 250.4816 250.6284 -0.14682343
1968 feb. 246.8155 245.3048 1.51067445
1968 mar. 252.0098 250.2414 1.76844760
1968 abr. 243.5690 242.5924 0.97664707
1968 may. 217.1874 219.0497 -1.86232409
1968 jun. 212.9136 213.9059 -0.99228068
1968 jul. 261.0586 260.2000 0.85860847
1968 ago. 262.4751 261.2415 1.23363112
1968 sep. 253.5156 251.2034 2.31221234
1968 oct. 249.8530 249.7370 0.11596421
1968 nov. 244.9549 247.3546 -2.39969168
1968 dic. 221.1463 223.7223 -2.57596850
1969 ene. 210.5977 210.7211 -0.12344490
1969 feb. 250.2505 248.7188 1.53169893
1969 mar. 248.2650 246.5228 1.74216893
1969 abr. 247.3034 246.3118 0.99162102
1969 may. 242.7937 244.8756 -2.08189129
1969 jun. 231.1652 232.2425 -1.07734199
1969 jul. 202.5800 201.9137 0.66627533
1969 ago. 200.8607 199.9167 0.94404388
1969 sep. 239.1268 236.9458 2.18097797
1969 oct. 236.9323 236.8223 0.10996733
1969 nov. 233.0740 235.3573 -2.28330088
1969 dic. 233.1221 235.8376 -2.71546567
1970 ene. 233.8208 233.9579 -0.13705746
1970 feb. 193.0105 191.8291 1.18135219
1970 mar. 189.3291 188.0005 1.32859354
1970 abr. 236.2696 235.3222 0.94737841
1970 may. 238.2240 240.2667 -2.04270733
1970 jun. 241.4517 242.5770 -1.12528208
1970 jul. 241.7054 240.9104 0.79495678
1970 ago. 233.5166 232.4191 1.09752638
1970 sep. 198.1050 196.2982 1.80683487
1970 oct. 186.0104 185.9241 0.08633296
1970 nov. 218.2276 220.3655 -2.13785867
1970 dic. 222.7274 225.3218 -2.59438556
1971 ene. 222.0213 222.1514 -0.13014101
1971 feb. 226.7373 225.3495 1.38778256
1971 mar. 231.3683 229.7447 1.62359843
1971 abr. 196.5834 195.7952 0.78824728
1971 may. 185.0438 186.6305 -1.58670129
1971 jun. 225.4641 226.5149 -1.05077210
1971 jul. 235.6982 234.9230 0.77519941
1971 ago. 236.1329 235.0231 1.10982297
1971 sep. 241.1509 238.9515 2.19943896
1971 oct. 230.3640 230.2571 0.10691879
1971 nov. 199.8989 201.8572 -1.95830223
1971 dic. 187.0570 189.2359 -2.17888764
1972 ene. 217.4684 217.5959 -0.12747227
1972 feb. 215.1564 213.8395 1.31689977
1972 mar. 215.4661 213.9541 1.51200671
1972 abr. 217.7978 216.9245 0.87331139
1972 may. 214.7996 216.6414 -1.84184934
1972 jun. 184.7269 185.5878 -0.86091698
1972 jul. 177.3159 176.7327 0.58318299
1972 ago. 207.7305 206.7542 0.97633189
1972 sep. 218.1746 216.1847 1.98988150
1972 oct. 224.6942 224.5899 0.10428727
1972 nov. 224.9024 227.1056 -2.20324810
1972 dic. 212.3656 214.8393 -2.47368867
1973 ene. 183.7487 183.8564 -0.10770697
1973 feb. 173.5839 172.5215 1.06244852
1973 mar. 221.1614 219.6094 1.55197277
1973 abr. 216.7209 215.8519 0.86899331
1973 may. 221.2251 223.1220 -1.89694629
1973 jun. 217.7267 218.7414 -1.01471206
1973 jul. 213.9397 213.2361 0.70363681
1973 ago. 186.2191 185.3439 0.87522846
1973 sep. 181.4456 179.7907 1.65489128
1973 oct. 222.1222 222.0191 0.10309352
1973 nov. 228.7036 230.9441 -2.24048641
1973 dic. 225.6833 228.3121 -2.62881663
1974 ene. 224.5799 224.7115 -0.13164077
1974 feb. 214.5551 213.2419 1.31321942
1974 mar. 191.7391 190.3936 1.34550542
1974 abr. 183.9389 183.2014 0.73754619
1974 may. 213.2147 215.0430 -1.82825925
1974 jun. 222.9096 223.9485 -1.03886689
1974 jul. 233.0016 232.2353 0.76633043
1974 ago. 224.6348 223.5790 1.05578198
1974 sep. 227.9454 225.8664 2.07899698
1974 oct. 193.2198 193.1301 0.08967906
1974 nov. 182.9607 184.7531 -1.79236778
1974 dic. 212.9383 215.4187 -2.48035962
1975 ene. 220.9596 221.0891 -0.12951868
1975 feb. 218.7275 217.3887 1.33875727
1975 mar. 218.7459 217.2109 1.53502230
1975 abr. 226.7615 225.8522 0.90925345
1975 may. 187.8364 189.4470 -1.61064709
1975 jun. 186.3019 187.1702 -0.86825725
1975 jul. 200.5634 199.9038 0.65964283
1975 ago. 186.9384 186.0598 0.87860916
1975 sep. 206.6377 204.7530 1.88465814
1975 oct. 211.9857 211.8873 0.09838887
1975 nov. 227.2132 229.4391 -2.22588577
1975 dic. 205.4283 207.8212 -2.39288123
1976 ene. 176.0597 176.1629 -0.10319995
1976 feb. 211.9239 210.6268 1.29711473
1976 mar. 215.3201 213.8091 1.51098217
1976 abr. 222.2635 221.3723 0.89121766
1976 may. 239.6066 241.6612 -2.05456276
1976 jun. 223.6820 224.7245 -1.04246665
1976 jul. 189.5637 188.9402 0.62346538
1976 ago. 182.0131 181.1576 0.85546029
1976 sep. 208.3546 206.4543 1.90031729
1976 oct. 211.8105 211.7122 0.09830756
1976 nov. 217.4702 219.6006 -2.13043882
1976 dic. 228.7774 231.4423 -2.66485750
1977 ene. 215.2890 215.4152 -0.12619478
1977 feb. 196.0592 194.8592 1.20001225
1977 mar. 187.2593 185.9452 1.31406898
1977 abr. 222.5512 221.6588 0.89237127
1977 may. 213.9397 215.7742 -1.83447593
1977 jun. 218.0656 219.0819 -1.01629150
1977 jul. 214.9400 214.2331 0.70692674
1977 ago. 212.5017 211.5029 0.99875649
1977 sep. 193.7848 192.0174 1.76743209
1977 oct. 213.2383 213.1393 0.09897024
1977 nov. 242.4983 244.8739 -2.37562569
1977 dic. 232.7346 235.4456 -2.71095197
1978 ene. 228.0617 228.1954 -0.13368168
1978 feb. 235.9962 234.5517 1.44445316
1978 mar. 224.0923 222.5198 1.57254000
1978 abr. 192.1555 191.3850 0.77049257
1978 may. 185.8437 187.4373 -1.59356021
1978 jun. 216.0966 217.1037 -1.00711501
1978 jul. 217.0111 216.2974 0.71373848
1978 ago. 219.8595 218.8262 1.03333810
1978 sep. 215.7580 213.7902 1.96784068
1978 oct. 219.1628 219.0611 0.10171998
1978 nov. 209.4520 211.5039 -2.05188882
1978 dic. 196.8496 199.1426 -2.29295434
1979 ene. 228.2496 228.3834 -0.13379182
1979 feb. 231.0967 229.6822 1.41446498
1979 mar. 214.1462 212.6435 1.50274447
1979 abr. 216.3581 215.4906 0.86753858
1979 may. 206.0286 207.7952 -1.76664035
1979 jun. 189.0453 189.9263 -0.88104282
1979 jul. 204.0398 203.3687 0.67107654
1979 ago. 230.4448 229.3617 1.08308894
1979 sep. 219.4981 217.4961 2.00195260
1979 oct. 192.9426 192.8530 0.08955040
1979 nov. 166.6984 168.3315 -1.63305475
1979 dic. 166.8567 168.8003 -1.94358939
1980 ene. 173.7280 173.8298 -0.10183319
1980 feb. 188.5128 187.3590 1.15382328
1980 mar. 191.2730 189.9308 1.34223462
1980 abr. 186.2401 185.4933 0.74677339
1980 may. 186.3702 187.9683 -1.59807481
1980 jun. 186.8963 187.7673 -0.87102744
1980 jul. 200.5909 199.9312 0.65973328
1980 ago. 200.9169 199.9726 0.94430802
1980 sep. 185.8142 184.1195 1.69473550
1980 oct. 181.5152 181.4310 0.08424661
1980 nov. 207.7288 209.7638 -2.03500756
1980 dic. 211.9029 214.3712 -2.46829902
1981 ene. 217.3375 217.4649 -0.12739554
1981 feb. 240.0782 238.6088 1.46943771
1981 mar. 270.5984 268.6995 1.89889080
1981 abr. 213.7970 212.9397 0.85726925
1981 may. 199.6298 201.3416 -1.71177235
1981 jun. 266.6636 267.9064 -1.24278176
1981 jul. 334.2323 333.1330 1.09927306
1981 ago. 355.9934 354.3202 1.67316647
1981 sep. 359.6376 356.3575 3.28010780
1981 oct. 344.9455 344.7854 0.16009947
1981 nov. 231.7517 234.0220 -2.27034701
1981 dic. 198.9816 201.2994 -2.31778842
1982 ene. 240.4876 240.6286 -0.14096530
1982 feb. 234.3650 232.9305 1.43446914
1982 mar. 232.4669 230.8356 1.63130772
1982 abr. 252.5806 251.5678 1.01278119
1982 may. 236.6652 238.6945 -2.02934104
1982 jun. 193.1498 194.0500 -0.90017179
1982 jul. 194.2231 193.5843 0.63878991
1982 ago. 242.2226 241.0842 1.13844451
1982 sep. 321.3744 318.4433 2.93112476
1982 oct. 255.2239 255.1054 0.11845701
1982 nov. 270.3896 273.0385 -2.64886178
1982 dic. 271.3053 274.4655 -3.16023332
1983 ene. 253.3185 253.4670 -0.14848632
1983 feb. 276.5050 274.8126 1.69239387
1983 mar. 276.9556 275.0121 1.94350167
1983 abr. 233.6807 232.7437 0.93699761
1983 may. 249.1498 251.2862 -2.13639316
1983 jun. 288.4134 289.7575 -1.34414638
1983 jul. 295.3155 294.3442 0.97127768
1983 ago. 284.5084 283.1712 1.33718748
1983 sep. 255.6298 253.2983 2.33149509
1983 oct. 235.0845 234.9754 0.10910971
1983 nov. 250.7871 253.2439 -2.45682661
1983 dic. 264.2366 267.3145 -3.07789530
1984 ene. 262.9030 263.0571 -0.15410442
1984 feb. 254.4558 252.8984 1.55743815
1984 mar. 226.2726 224.6848 1.58783998
1984 abr. 187.2935 186.5425 0.75099724
1984 may. 228.4104 230.3690 -1.95855833
1984 jun. 244.6376 245.7777 -1.14012992
1984 jul. 234.3622 233.5914 0.77080537
1984 ago. 222.2031 221.1587 1.04435301
1984 sep. 223.6370 221.5973 2.03970182
1984 oct. 198.7995 198.7072 0.09226876
1984 nov. 190.2322 192.0958 -1.86360276
1984 dic. 233.6854 236.4074 -2.72202714
1985 ene. 249.5595 249.7058 -0.14628293
1985 feb. 232.5093 231.0862 1.42311102
1985 mar. 228.7880 227.1825 1.60549149
1985 abr. 226.8875 225.9777 0.90975868
1985 may. 205.5388 207.3012 -1.76244045
1985 jun. 195.3550 196.2654 -0.91044909
1985 jul. 240.3825 239.5919 0.79060583
1985 ago. 267.3741 266.1174 1.25665639
1985 sep. 245.9622 243.7189 2.24332086
1985 oct. 228.4703 228.3643 0.10603987
1985 nov. 227.9607 230.1939 -2.23320862
1985 dic. 206.5699 208.9761 -2.40617888
1986 ene. 212.7804 212.9051 -0.12472433
1986 feb. 225.9160 224.5332 1.38275566
1986 mar. 252.5818 250.8093 1.77246154
1986 abr. 232.3766 231.4448 0.93176851
1986 may. 231.1226 233.1044 -1.98181472
1986 jun. 235.9310 237.0306 -1.09955293
1986 jul. 203.9780 203.3071 0.67087328
1986 ago. 184.9602 184.0909 0.86931164
1986 sep. 224.6693 222.6202 2.04911701
1986 oct. 228.7992 228.6930 0.10619252
1986 nov. 227.9562 230.1894 -2.23316454
1986 dic. 238.8287 241.6106 -2.78193761
1987 ene. 233.9756 234.1127 -0.13714820
1987 feb. 198.2491 197.0357 1.21341589
1987 mar. 191.2746 189.9324 1.34224585
1987 abr. 225.5667 224.6622 0.90446262
1987 may. 226.6133 228.5564 -1.94314867
1987 jun. 229.4129 230.4821 -1.06917543
1987 jul. 233.7905 233.0216 0.76892508
1987 ago. 226.8814 225.8151 1.06634098
1987 sep. 197.2121 195.4134 1.79869109
1987 oct. 193.7951 193.7052 0.08994607
1987 nov. 242.6637 245.0409 -2.37724602
1987 dic. 265.2901 268.3803 -3.09016674
1988 ene. 247.5289 247.6740 -0.14509266
1988 feb. 234.7004 233.2639 1.43652201
1988 mar. 230.9320 229.3115 1.62053675
1988 abr. 204.1518 203.3332 0.81859455
1988 may. 194.5951 196.2637 -1.66860114
1988 jun. 230.4745 231.5486 -1.07412300
1988 jul. 238.2018 237.4184 0.78343362
1988 ago. 237.5993 236.4826 1.11671504
1988 sep. 235.8808 233.7294 2.15137252
1988 oct. 232.3987 232.2908 0.10786315
1988 nov. 201.7302 203.7064 -1.97624249
1988 dic. 192.7974 195.0432 -2.24575328
1989 ene. 224.9014 225.0332 -0.13182923
1989 feb. 228.1034 226.7073 1.39614400
1989 mar. 230.3603 228.7438 1.61652491
1989 abr. 227.1137 226.2030 0.91066568
1989 may. 189.7474 191.3744 -1.62703340
1989 jun. 195.7940 196.7065 -0.91249504
1989 jul. 190.9498 190.3218 0.62802419
1989 ago. 193.3320 192.4233 0.90865904
1989 sep. 231.8456 229.7310 2.11456911
1989 oct. 230.9037 230.7965 0.10716928
1989 nov. 229.9708 232.2237 -2.25290049
1989 dic. 201.6816 204.0308 -2.34923871
1990 ene. 201.3522 201.4702 -0.11802552
1990 feb. 198.1761 196.9631 1.21296908
1990 mar. 234.7253 233.0781 1.64715576
1990 abr. 238.8940 237.9361 0.95790156
1990 may. 239.8383 241.8948 -2.05654952
1990 jun. 246.1514 247.2986 -1.14718496
1990 jul. 246.8755 246.0635 0.81196098
1990 ago. 217.5938 216.5711 1.02268933
1990 sep. 215.6670 213.7000 1.96701070
1990 oct. 249.8857 249.7697 0.11597939
1990 nov. 252.6153 255.0900 -2.47473651
1990 dic. 252.3634 255.3030 -2.93959324
1991 ene. 253.3502 253.4987 -0.14850490
1991 feb. 248.7497 247.2272 1.52251304
1991 mar. 218.3455 216.8133 1.53221254
1991 abr. 204.7160 203.8951 0.82085685
1991 may. 241.3544 243.4239 -2.06954968
1991 jun. 242.0164 243.1443 -1.12791385
1991 jul. 236.4637 235.6860 0.77771710
1991 ago. 228.9592 227.8831 1.07610663
1991 sep. 224.6305 222.5817 2.04876313
1991 oct. 200.9991 200.9058 0.09328966
1991 nov. 193.5075 195.4032 -1.89568911
1991 dic. 229.0623 231.7305 -2.66817609
1992 ene. 236.3771 236.5157 -0.13855587
1992 feb. 233.9410 232.5091 1.43187398
1992 mar. 234.4899 232.8444 1.64550387
1992 abr. 233.5659 232.6294 0.93653729
1992 may. 207.6669 209.4476 -1.78068834
1992 jun. 202.3111 203.2540 -0.94286789
1992 jul. 238.0510 237.2681 0.78293765
1992 ago. 238.9175 237.7946 1.12291057
1992 sep. 242.2927 240.0828 2.20985285
1992 oct. 246.6604 246.5459 0.11448243
1992 nov. 243.8486 246.2375 -2.38885385
1992 dic. 213.0383 215.5198 -2.48152445
1993 ene. 211.7129 211.8370 -0.12409860
1993 feb. 244.5876 243.0906 1.49703823
1993 mar. 247.0114 245.2780 1.73337195
1993 abr. 242.9970 242.0226 0.97435350
1993 may. 244.0875 246.1805 -2.09298528
1993 jun. 246.6715 247.8211 -1.14960888
1993 jul. 214.4323 213.7270 0.70525694
1993 ago. 208.7197 207.7387 0.98098112
1993 sep. 218.5046 216.5117 1.99289129
1993 oct. 247.7782 247.6632 0.11500124
1993 nov. 253.0994 255.5789 -2.47947897
1993 dic. 255.6167 258.5942 -2.97748851
1994 ene. 249.2126 249.3587 -0.14607959
1994 feb. 224.1366 222.7647 1.37186455
1994 mar. 215.3582 213.8470 1.51124953
1994 abr. 253.3206 252.3049 1.01574840
1994 may. 256.7568 258.9584 -2.20162115
1994 jun. 256.5883 257.7841 -1.19582598
1994 jul. 264.8974 264.0262 0.87123409
1994 ago. 251.6948 250.5118 1.18296379
1994 sep. 223.0494 221.0151 2.03434256
1994 oct. 217.6888 217.5878 0.10103585
1994 nov. 257.3576 259.8788 -2.52119427
1994 dic. 273.7745 276.9635 -3.18899520
1995 ene. 260.2476 260.4001 -0.15254792
1995 feb. 255.3896 253.8264 1.56315363
1995 mar. 253.5915 251.8120 1.77954698
1995 abr. 230.8741 229.9484 0.92574389
1995 may. 232.2953 234.2872 -1.99187031
1995 jun. 267.0056 268.2500 -1.24437565
1995 jul. 266.8101 265.9326 0.87752487
1995 ago. 257.0865 255.8782 1.20830474
1995 sep. 259.8669 257.4968 2.37013996
1995 oct. 261.0520 260.9308 0.12116200
1995 nov. 230.4067 232.6639 -2.25717077
1995 dic. 224.9148 227.5347 -2.61986495
1996 ene. 253.2600 253.4085 -0.14845203
1996 feb. 254.9725 253.4119 1.56060070
1996 mar. 269.5991 267.7072 1.89187834
1996 abr. 269.9806 268.8980 1.08255058
1996 may. 259.6547 261.8812 -2.22646988
1996 jun. 239.0290 240.1430 -1.11399112
1996 jul. 232.9397 232.1736 0.76612684
1996 ago. 270.8287 269.5558 1.27289298
1996 sep. 276.1827 273.6638 2.51894971
1996 oct. 265.5232 265.4000 0.12323722
1996 nov. 271.8050 274.4677 -2.66272770
1996 dic. 268.2355 271.3600 -3.12447551
1997 ene. 235.4825 235.6205 -0.13803149
1997 feb. 227.7748 226.3807 1.39413275
1997 mar. 270.8278 268.9273 1.90050059
1997 abr. 280.9846 279.8579 1.12667370
1997 may. 279.2355 281.6299 -2.39437002
1997 jun. 281.3544 282.6656 -1.31124802
1997 jul. 265.2748 264.4023 0.87247534
1997 ago. 235.1568 234.0516 1.10523530
1997 sep. 223.2160 221.1801 2.03586205
1997 oct. 254.0725 253.9546 0.11792261
1997 nov. 258.4046 260.9361 -2.53145117
1997 dic. 249.8841 252.7948 -2.91071372
1998 ene. 251.6187 251.7662 -0.14748996
1998 feb. 275.3148 273.6297 1.68510906
1998 mar. 239.5769 237.8957 1.68120126
1998 abr. 233.6350 232.6982 0.93681436
1998 may. 268.6197 270.9230 -2.30334236
1998 jun. 262.4816 263.7049 -1.22329161
1998 jul. 258.8155 257.9643 0.85123103
1998 ago. 264.0098 262.7690 1.24084420
1998 sep. 255.5690 253.2381 2.33094056
1998 oct. 229.1874 229.0810 0.10637269
1998 nov. 224.9136 227.1170 -2.20335782
1998 dic. 273.0586 276.2393 -3.18065621
1999 ene. 274.4751 274.6360 -0.16088757
1999 feb. 265.5156 263.8905 1.62513146
1999 mar. 261.8530 260.0155 1.83752104
1999 abr. 256.9549 255.9246 1.03032097
1999 may. 233.1463 235.1455 -1.99916741
1999 jun. 222.5977 223.6351 -1.03741329
1999 jul. 262.2505 261.3880 0.86252857
1999 ago. 260.2650 259.0418 1.22324367
1999 sep. 259.3034 256.9384 2.36500050
1999 oct. 254.7937 254.6754 0.11825734
1999 nov. 243.1652 245.5474 -2.38215895
1999 dic. 214.5800 217.0795 -2.49948256
2000 ene. 212.8607 212.9855 -0.12477140
2000 feb. 251.1268 249.5897 1.53706246
2000 mar. 248.9323 247.1854 1.74685162
2000 abr. 245.0740 244.0913 0.98268172
2000 may. 245.1221 247.2240 -2.10185670
2000 jun. 245.8208 246.9664 -1.14564420
2000 jul. 205.0105 204.3362 0.67426912
2000 ago. 201.3291 200.3829 0.94624535
2000 sep. 248.2696 246.0052 2.26436571
2000 oct. 250.2240 250.1079 0.11613641
2000 nov. 253.4517 255.9346 -2.48293027
2000 dic. 253.7054 256.6606 -2.95522520
2001 ene. 245.5166 245.6605 -0.14391313
2001 feb. 210.1050 208.8190 1.28598186
2001 mar. 198.0104 196.6209 1.38951349
2001 abr. 230.2276 229.3044 0.92315159
2001 may. 234.7274 236.7401 -2.01272492
2001 jun. 234.0213 235.1120 -1.09065281
2001 jul. 238.7373 237.9521 0.78519485
2001 ago. 243.3683 242.2245 1.14382930
2001 sep. 208.5834 206.6810 1.90240408
2001 oct. 197.0438 196.9523 0.09145389
2001 nov. 237.4641 239.7904 -2.32630833
2001 dic. 247.6982 250.5835 -2.88525180
2002 ene. 248.1329 248.2783 -0.14544671
2002 feb. 253.1509 251.6014 1.54945130
2002 mar. 242.3640 240.6632 1.70075939
2002 abr. 211.8989 211.0492 0.84965837
2002 may. 199.0570 200.7639 -1.70686074
2002 jun. 229.4684 230.5378 -1.06943408
2002 jul. 227.1564 226.4093 0.74710586
2002 ago. 227.4661 226.3970 1.06908907
2002 sep. 229.7978 227.7019 2.09589196
2002 oct. 226.7996 226.6943 0.10526444
2002 nov. 196.7269 198.6541 -1.92722785
2002 dic. 189.3159 191.5211 -2.20519988
2003 ene. 219.7305 219.8593 -0.12879823
2003 feb. 230.1746 228.7658 1.40882111
2003 mar. 236.6942 235.0332 1.66097227
2003 abr. 236.9024 235.9525 0.94991577
2003 may. 224.3656 226.2895 -1.92387524
2003 jun. 195.7487 196.6610 -0.91228392
2003 jul. 185.5839 184.9735 0.61037602
2003 ago. 233.1614 232.0655 1.09585694
2003 sep. 228.7209 226.6348 2.08607000
2003 oct. 233.2251 233.1169 0.10824671
2003 nov. 229.7267 231.9772 -2.25050918
2003 dic. 225.9397 228.5715 -2.63180325
2004 ene. 198.2191 198.3353 -0.11618901
2004 feb. 193.4456 192.2616 1.18401529
2004 mar. 234.1222 232.4793 1.64292358
2004 abr. 240.7036 239.7384 0.96515757
2004 may. 237.6833 239.7214 -2.03807097
2004 jun. 236.5799 237.6825 -1.10257712
2004 jul. 226.5551 225.8100 0.74512822
2004 ago. 203.7391 202.7815 0.95757234
2004 sep. 195.9389 194.1518 1.78707875
2004 oct. 225.2147 225.1102 0.10452885
2004 nov. 234.9096 237.2109 -2.30128327
2004 dic. 245.0016 247.8554 -2.85384112
2005 ene. 236.6348 236.7735 -0.13870693
2005 feb. 239.9454 238.4768 1.46862488
2005 mar. 205.2198 203.7797 1.44010456
2005 abr. 194.9607 194.1790 0.78174068
2005 may. 224.9383 226.8671 -1.92878599
2005 jun. 232.9596 234.0453 -1.08570477
2005 jul. 230.7275 229.9686 0.75885103
2005 ago. 230.7459 229.6614 1.08450411
2005 sep. 238.7615 236.5839 2.17764621
2005 oct. 199.8364 199.7436 0.09275002
2005 nov. 198.3019 200.2446 -1.94265728
2005 dic. 212.5634 215.0394 -2.47599269
2006 ene. 198.9384 199.0550 -0.11661064
2006 feb. 218.6377 217.2995 1.33820764
2006 mar. 223.9857 222.4139 1.57179194
2006 abr. 239.2132 238.2540 0.95918146
2006 may. 217.4283 219.2927 -1.86438975
2006 jun. 188.0597 188.9361 -0.87644945
2006 jul. 223.9239 223.1874 0.73647433
2006 ago. 227.3201 226.2517 1.06840287
2006 sep. 234.2635 232.1269 2.13662179
2006 oct. 251.6066 251.4898 0.11677811
2006 nov. 235.6820 237.9909 -2.30885005
2006 dic. 201.5637 203.9116 -2.34786538
2007 ene. 194.0131 194.1268 -0.11372360
2007 feb. 220.3546 219.0059 1.34871620
2007 mar. 223.8105 222.2399 1.57056250
2007 abr. 229.4702 228.5501 0.92011462
2007 may. 240.7774 242.8420 -2.06460206
2007 jun. 227.2890 228.3483 -1.05927702
2007 jul. 208.0592 207.3749 0.68429614
2007 ago. 199.2593 198.3228 0.93651731
2007 sep. 234.5512 232.4120 2.13924579
2007 oct. 225.9397 225.8348 0.10486534
2007 nov. 230.0656 232.3194 -2.25382920
2007 dic. 226.9400 229.5835 -2.64345500
2008 ene. 224.5017 224.6333 -0.13159494
2008 feb. 205.7848 204.5253 1.25953937
2008 mar. 225.2383 223.6577 1.58058191
2008 abr. 254.4983 253.4778 1.02047066
2008 may. 244.7346 246.8331 -2.09853399
2008 jun. 240.0617 241.1805 -1.11880400
2008 jul. 247.9962 247.1806 0.81564690
2008 ago. 236.0923 234.9827 1.10963215
2008 sep. 204.1555 202.2935 1.86201901
2008 oct. 197.8437 197.7519 0.09182515
2008 nov. 228.0966 230.3311 -2.23453996
2008 dic. 229.0111 231.6787 -2.66757970
2009 ene. 231.8595 231.9954 -0.13590782
2009 feb. 227.7580 226.3640 1.39402992
2009 mar. 231.1628 229.5406 1.62215636
2009 abr. 221.4520 220.5640 0.88796376
2009 may. 208.8496 210.6404 -1.79082968
2009 jun. 240.2496 241.3693 -1.11967971
2009 jul. 243.0967 242.2972 0.79953270
2009 ago. 226.1462 225.0833 1.06288555
2009 sep. 228.3581 226.2753 2.08276105
2009 oct. 218.0286 217.9274 0.10119356
2009 nov. 201.0453 203.0148 -1.96953289
2009 dic. 216.0398 218.5563 -2.51648669
2010 ene. 242.4448 242.5869 -0.14211255
2010 feb. 231.4981 230.0812 1.41692181
2010 mar. 204.9426 203.5044 1.43815935
2010 abr. 178.6984 177.9819 0.71653317
2010 may. 178.8567 180.3903 -1.53364855
2010 jun. 185.7280 186.5936 -0.86558260
2010 jul. 200.5128 199.8533 0.65947641
2010 ago. 203.2730 202.3176 0.95538167
2010 sep. 198.2401 196.4320 1.80806706
2010 oct. 198.3702 198.2781 0.09206951
Elecmensual_20190618.ts2 <- 
  ts(
    Elecmensual_20190618.xls$Demand,
    start = c(1950,1),
    deltat = 1/12
    )

fit <- stl(Elecmensual_20190618.ts2, s.window = 7)

autoplot(Elecmensual_20190618.ts, series = "Originales") + 
  autolayer(seasadj(fit),
            series = "Datos desestacionalizados")